Voxel-Based Diagnosis of Alzheimer's Disease Using Classifier Ensembles

Abstract

Functional magnetic resonance imaging (fMRI) is one of the most promising noninvasive techniques for early Alzheimer's disease (AD) diagnosis. In this paper, we explore the application of different machine learning techniques to the classification of fMRI data for this purpose. The functional images were first preprocessed using the statistical parametric mapping toolbox to output individual maps of statistically activated voxels. A fast filter was applied afterwards to select voxels commonly activated across demented and nondemented groups. Four feature ranking selection techniques were embedded into a wrapper scheme using an inner-outer loop for the selection of relevant voxels. The wrapper approach was guided by the performance of six pattern recognition models, three of which were ensemble classifiers based on stochastic searches. Final classification performance was assessed from the nested internal and external cross-validation loops taking several voxel sets ordered by importance. Numerical performance was evaluated using statistical tests, and the best combination of voxel selection and classification reached a 97.14% average accuracy. Results repeatedly pointed out Brodmann regions with distinct activation patterns between demented and nondemented profiles, indicating that the machine learning analysis described is a powerful method to detect differences in several brain regions between both groups.

Disease Diagnosis in Smart Healthcare: Innovation, Technologies and Applications

“…Table 4 shows some studies in Alzheimer's disease and other forms of dementia via machine learning algorithms. The applications include diagnosis of Alzheimer's disease [115,116], diagnosis of dementias [117], and detection of Alzheimer's disease related regions [118], prediction of mild cognitive impairment patients for conversion to Alzheimer's disease [119,120], detection of dissociable multivariate morphological patterns [121], diagnosis of both Alzheimer's disease and mild cognitive impairment [122] and identification of genes related to Alzheimer's disease [125,126]. Alzheimer's disease: sensitivity = 85%, specificity = 82%, accuracy = 85%; Mild cognitive impairment: sensitivity = 84%, specificity = 81%, accuracy = 85% [125] Identification of genes related to Alzheimer's disease DT; QAR 33 90 genes are related to Alzheimer's disease [126] Identification of genes related to Alzheimer's disease ELM; RF; SVM 31 Sensitivity= 78.77%; Specificity= 83.1%; Accuracy = 74.67% DCNN = deep convolutional neural network; DT = decision tree; ELM = extreme learning machine; EM = expectation maximization; GA = genetic algorithm; LC = lasso classification; LDS = low density separation; LR = logistic regression; NBC = Naive Bayes classifier; QAR = quantitative association rules; RF = random forest; RLO = random linear oracle; RS = random subspace; SVM = support vector machine.…”

“…Fourteen different algorithms were employed in [115][116][117][118][119][120][121][122][123][124][125][126]. The datasets of Alzheimer's disease and other forms of dementia have relatively small sample size.…”

“…Due to the multitude of smart healthcare applications, only four applications in the field of diseases diagnosis, cardiovascular diseases [82][83][84][85][86][87][88][89][90][91][92][93][94][95][96][97][98][99], diabetes mellitus [100][101][102][103][104][105][106][107][108][109][110][111][112], Alzheimer's disease and other forms of dementias [113][114][115][116][117][118][119][120][121][122][123][124][125][126], and tuberculosis [127][128][129][130][13...…”

A New Hybrid Feature Subset Selection Framework Based on Binary Genetic Algorithm and Information Theory

“…11 Nowadays, nature-inspired techniques and machine learning approaches have increasingly been used for disease diagnosis. 12 The fundamental characteristics of FS reduce the dimension and remove the redundancies of the gene expression data during the clas-si¯cation process. By classifying the gene expression data, the topmost signi¯cant genes are discovered which provides useful information in cancer treatment.…”

A Non Invasive Biomarkers for Alzheimer’s disease Detection

“…Machine learning tools were employed to detect the selectively affected brain regions using the data gathered in a fMRI study of demented and non-demented patients. For analysing these kinds of data, SVM-based classifier (sequential minimal optimization-SMO) and voxel selector (Random feature elimination-RFE) was highlighted as the best-suited data mining approaches [11]. Optimally-discriminative voxel-based analysis (ODVBA) frame work was proposed to precisely describe the shape and location of structural abnormality in the brain region of the MRI images.…”